savitzky_golay(y, window_size, order, deriv=0, use_fft=True)¶
Deprecated since version 1.0: The savitzky_golay function is deprecated and may be removed in a future version. Use scipy.signal.savgol_filter instead.
Smooth (and optionally differentiate) data with a Savitzky-Golay filter
This implementation is based on [R601615d55566-1].
The Savitzky-Golay filter removes high frequency noise from data. It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techhniques.
- yarray_like, shape (N,)
the values of the time history of the signal.
the length of the window. Must be an odd integer number.
the order of the polynomial used in the filtering. Must be less then window_size - 1.
- deriv: int
the order of the derivative to compute (default = 0 means only smoothing)
if True (default) then convolue using FFT for speed
- y_smoothndarray, shape (N)
the smoothed signal (or it’s n-th derivative).
The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point.
>>> t = np.linspace(-4, 4, 500) >>> y = np.exp(-t ** 2) >>> y_smooth = savitzky_golay(y, window_size=31, order=4)